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Robust Evolutionary Algorithm Design for Socio-economic Simulation

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Author Info

  • Floortje Alkemade

    ()

  • Han Poutré

    ()

  • Hans Amman

    ()

Abstract

Agent-based computational economics (ACE) combines elements from economics and computer science. In this paper, we focus on the relation between the evolutionary technique that is used and the economic problem that is modeled. In the field of ACE, economic simulations often derive parameter settings for the evolutionary algorithm directly from the values of the economic model parameters. In this paper, we compare two important approaches that are dominating ACE research and show that the above practice may hinder the performance of the evolutionary algorithm and thereby hinder agent learning. More specifically, we show that economic model parameters and evolutionary algorithm parameters should be treated separately by comparing the two widely used approaches to social learning with respect to their convergence properties and robustness. This leads to new considerations for the methodological aspects of evolutionary algorithm design within the field of ACE. Copyright Springer 2006

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File URL: http://hdl.handle.net/10.1007/s10614-006-9051-5
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Bibliographic Info

Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 28 (2006)
Issue (Month): 4 (November)
Pages: 355-370

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Handle: RePEc:kap:compec:v:28:y:2006:i:4:p:355-370

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Web page: http://www.springerlink.com/link.asp?id=100248
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Related research

Keywords: evolutionary algorithms; simulation;

References

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  1. Fudenberg, Drew & Levine, David, 1998. "Learning in games," European Economic Review, Elsevier, vol. 42(3-5), pages 631-639, May.
  2. Fernando Vega-Redondo, 1997. "The Evolution of Walrasian Behavior," Econometrica, Econometric Society, vol. 65(2), pages 375-384, March.
  3. C. Lawrenz & F. Westerhoff, 2003. "Modeling Exchange Rate Behavior with a Genetic Algorithm," Computational Economics, Society for Computational Economics, vol. 21(3), pages 209-229, June.
  4. Tesfatsion, Leigh S., 2001. "Introduction to the Special Issue on Agent-Based Computational Economics," Staff General Research Papers 1915, Iowa State University, Department of Economics.
  5. Bullard, James & Duffy, John, 1999. "Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs," Computational Economics, Society for Computational Economics, vol. 13(1), pages 41-60, February.
  6. Klos, Tomas B., 1997. "Decentralized interaction and co-adaptation in the repeated prisoner's dilemma," Research Report 97B33, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
  7. Jorgen W. Weibull, 1997. "Evolutionary Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262731215, December.
  8. F. Alkemade & J.A. La Poutre & D.D.B. van Bragt, 2000. "Stabilization Of Tag-Mediated Interaction By Sexual Reproduction In An Evolutionary Agent System," Computing in Economics and Finance 2000 172, Society for Computational Economics.
  9. Riechmann, Thomas, 2001. "Genetic algorithm learning and evolutionary games," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 1019-1037, June.
  10. Murat Yildizoglu, 1999. "Competing R&D Strategies in an Evolutionary Industry Model," Computing in Economics and Finance 1999 343, Society for Computational Economics.
  11. Paul McNelis & John Duffy, 1998. "Approximating and Simulating the Stochastic Growth Model: Parameterized Expectations, Neural Networks, and the Genetic Algorithm," GE, Growth, Math methods 9804004, EconWPA, revised 04 May 1998.
  12. Vriend, Nicolaas J., 2000. "An illustration of the essential difference between individual and social learning, and its consequences for computational analyses," Journal of Economic Dynamics and Control, Elsevier, vol. 24(1), pages 1-19, January.
  13. Chen, Shu-Heng & Yeh, Chia-Hsuan, 2001. "Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 363-393, March.
  14. Eric Ringhut & Stefan Kooths, 2003. "Modeling Expectations with GENEFER – an Artificial Intelligence Approach," Computational Economics, Society for Computational Economics, vol. 21(1), pages 173-194, February.
  15. Herbert Dawid, 1996. "Learning of cycles and sunspot equilibria by Genetic Algorithms (*)," Journal of Evolutionary Economics, Springer, vol. 6(4), pages 361-373.
  16. David F. Midgley & Robert E. Marks & Lee C. Cooper, 1997. "Breeding Competitive Strategies," Management Science, INFORMS, vol. 43(3), pages 257-275, March.
  17. Arifovic, Jasmina, 2001. "Evolutionary dynamics of currency substitution," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 395-417, March.
  18. Alemdar, Nedim M. & Sirakaya, Sibel, 2003. "On-line computation of Stackelberg equilibria with synchronous parallel genetic algorithms," Journal of Economic Dynamics and Control, Elsevier, vol. 27(8), pages 1503-1515, June.
  19. Marimon, Ramon & McGrattan, Ellen & Sargent, Thomas J., 1990. "Money as a medium of exchange in an economy with artificially intelligent agents," Journal of Economic Dynamics and Control, Elsevier, vol. 14(2), pages 329-373, May.
  20. Jasmina Arifovic & Michael Maschek, 2006. "Revisiting Individual Evolutionary Learning in the Cobweb Model – An Illustration of the Virtual Spite-Effect," Computational Economics, Society for Computational Economics, vol. 28(4), pages 333-354, November.
  21. Mailath, George J., 1992. "Introduction: Symposium on evolutionary game theory," Journal of Economic Theory, Elsevier, vol. 57(2), pages 259-277, August.
  22. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
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Citations

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Cited by:
  1. William Tracy, 2014. "Paradox Lost: The Evolution of Strategies in Selten’s Chain Store Game," Computational Economics, Society for Computational Economics, vol. 43(1), pages 83-103, January.
  2. Waltman, L. & van Eck, N.J.P., 2009. "A Mathematical Analysis of the Long-run Behavior of Genetic Algorithms for Social Modeling," ERIM Report Series Research in Management ERS-2009-011-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.
  3. Waltman, L. & van Eck, N.J.P. & Dekker, R. & Kaymak, U., 2009. "Economic Modeling Using Evolutionary Algorithms: The Effect of a Binary Encoding of Strategies," ERIM Report Series Research in Management ERS-2009-028-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.
  4. Steven Kimbrough & Frederic Murphy, 2009. "Learning to Collude Tacitly on Production Levels by Oligopolistic Agents," Computational Economics, Society for Computational Economics, vol. 33(1), pages 47-78, February.
  5. Christopher Boyer & B. Brorsen, 2014. "Implications of a Reserve Price in an Agent-Based Common-Value Auction," Computational Economics, Society for Computational Economics, vol. 43(1), pages 33-51, January.
  6. Jasmina Arifovic & Michael Maschek, 2006. "Revisiting Individual Evolutionary Learning in the Cobweb Model – An Illustration of the Virtual Spite-Effect," Computational Economics, Society for Computational Economics, vol. 28(4), pages 333-354, November.
  7. Jaroslaw Stanczak, 2009. "Application of an evolutionary algorithmto simulation of the co2 emission permits marketwith purchase prices," Operations Research and Decisions, Wroclaw University of Technology, Institute of Organization and Management, vol. 4, pages 94-108.
  8. Christopher Boyer & B. Brorsen & Tong Zhang, 2014. "Common-value auction versus posted-price selling: an agent-based model approach," Journal of Economic Interaction and Coordination, Springer, vol. 9(1), pages 129-149, April.
  9. Ludo Waltman & Nees Eck, 2009. "Robust Evolutionary Algorithm Design for Socio-Economic Simulation: Some Comments," Computational Economics, Society for Computational Economics, vol. 33(1), pages 103-105, February.
  10. Tong Zhang & B. Brorsen, 2009. "Particle Swarm Optimization Algorithm for Agent-Based Artificial Markets," Computational Economics, Society for Computational Economics, vol. 34(4), pages 399-417, November.

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